Tolerance of Non-Smokers to Smokers: Analysis of statistics


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Furthermore, no communication with the outside world is permitted, which poses major psychological stress. This situation offers a particularly interesting area of clinical study. Submarines are one of the only places where subjects are completely cutoff from the outside world for several months at a time. Submarine physicians general medicine specialists trained in remote medicine have noticed that submariner smokers who abruptly stop their tobacco addiction at the beginning of a mission do not suffer from withdrawal symptoms.

However, they have found that submariners quickly resume smoking after their operational patrol has ended. This paradox has not yet been scientifically investigated. Studies into smoking in confined environments are rare, as they normally investigate passive smoking. This original study seeks to quantitatively measure withdrawal symptoms in smokers during a submarine patrol and to clarify the factors that promote smoking resumption upon their return to the surface. Between November and February , the all-male crews of two submarines departing on a mission of approximately 70 days were offered the opportunity to participate in the study.

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Comparison of Physical Fitness among Smoker and Non-Smoker Men

Inclusion criteria were their continued presence on board during the selected mission and their voluntary participation. Noninclusion criterion was the refusal to participate in the study. The study consisted of five questionnaires. The first questionnaire was administered during the medical examination before the patrol.

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The second questionnaire was completed 48 hours after beginning the mission, the third questionnaire was completed in the middle of the patrol, and the fourth questionnaire was completed 1 week before completing the patrol. The evaluation scores chosen in this study are validated scores.


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The Mood and Physical Symptoms Scale has proven to provide linkages between each symptom and smoking cessation. In the literature, three scales emerge: Their Cronbach indices are satisfactory 0. The MNWS was chosen because of its power and reliability, and it can be administered more quickly. A score that progressively decreases during smoking cessation indicates the presence of withdrawal symptoms. In measuring the withdrawal score, it seemed of paramount importance not to forget social influences e. This score should not be interpreted in absolute terms, as it was used in situations for which it had not been validated, but rather as a measure investigating whether smokers who were in withdrawal were more anxious and depressed than nonsmokers.

Finally, only the smokers were invited to complete the fifth questionnaire during the 2 months following their return. This final questionnaire sought to evaluate the importance of smoking resumption after the patrol and reasons for smoking cessation failure. A maximum of 6 months elapsed between inclusion in the study and the final questionnaire. A verbal explanation of the study was first given to the entire cohort and was then given individually.

Each study participant signed a written consent form in the presence of a physician investigator. Data collection was conducted in compliance with the Helsinki Declaration by assigning anonymous numbers and not collecting the participants' names. Mean comparisons quantitative variables were conducted using the nonparametric Mann—Whitney and Kruskal—Wallis tests. Percent comparisons qualitative variables were conducted using the chi-squared and Fisher's exact tests.

Statistical analyses were performed using R software http: All statistical tests were two-sided. In total, members of the crew were offered participation in the study and subjects agreed to participate; 52 The population was divided into a smoking cohort and a nonsmoking cohort nonsmokers and ex-smokers.

All subjects completed the first four questionnaires, whereas only 36 of the 52 smokers A comparison of demographic factors between the two cohorts is provided in Table I. The smoking population smoked an average number of cigarettes Within the smoking cohort, There was no significant difference in the HAD score between the smoking and nonsmoking cohorts in the different questionnaires. Within the same population, there was also no significant variation in the HAD score among the different questionnaires. Analysis by age and rank subgroups did not offer any statistically relevant information.

For the smoking cohort in general, the MNWS was stable over time. There was no significant variation in weight before and after the mission.


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During the completion of the questionnaires before and after the operational patrol, the number of smokers wishing to take up smoking again after discharge increased. Among the 36 smokers who responded to the final questionnaire, 23 Therefore, there was a Those who resumed smoking and those who quit had the same mean age 29 years and the same distribution in rank.

To one of the closed questions in the fifth questionnaire, 12 of the smokers The latter mentioned in an open question, the presence of smokers in their environment and the feeling of freedom as one of the reasons for resuming smoking. Other commonly cited reasons for smoking resumption are inactivity upon return, absence of motivation, and a readily available supply of tobacco. An analysis of the kinetics of smoking resumption indicates that The attrition bias in the last questionnaire is important yet independent of resumption status.

Comparison of Physical Fitness among Smoker and Non-Smoker Men

The bias is due to transfers aboard other vessels at the end of the mission. Demographic data and smoking habits gathered from the first questionnaire and from those who responded to the fifth questionnaire as well as from other follow-up of smokers cannot be analyzed because of a low number of respondents.

In our study, there were no withdrawal symptoms in smokers during abrupt and forced smoking cessation. After 11 weeks of cessation, there is no longer a physical or psychological dependence on nicotine, indicating that the smoking resumption observed is solely attributable to a behavioral dependence. However, in literature, withdrawal symptoms usually became apparent because of the greater presence of signs of anxiety or depression in the smoking cohort, particularly during the first week following cessation. Moreover, in the presence of withdrawal symptoms, the MNWS generally decreases along with smoking cessation.

However, this was not the case in this study.

Personality and Smoking Behaviour of Non-Smokers, Previous Smokers, and Habitual Smokers

Several explanatory hypotheses are included below: In our study, and according to the doctors survey, However, the comparison of biographical elements and smoking habits is not feasible due to a lack of participants. As a result, we cannot conclude that the use of patches or not is efficient … even though we know that according to the relevant literature it is proven so.

Finally, according to the investigative practitioners in the two submarines, the subjective feeling of the participants using patches was very good, without any noted undesirable incident. During the very serious medical expertise appointments before the missions, all the smokers are systematically offered patches. It is worth noting that according to the first questionnaire, The patients may, therefore, have a feeling of efficiency from the patch in the prevention of withdrawal symptoms, but not in terms of efficiency of smoking cessation … although relevant data describes patches as efficient in both cases.

The beginning of a mission, when withdrawal symptoms are strongest, is often a period of intense professional activity for the submarine crew. It is by nature tiring and stressful, which likely obscures some of the sleep disorders observed during nicotine withdrawal. In some analyses we included as time-varying covariates the week of the study and two summaries of individual smoking history: We also included a drug-by-week interaction to examine variation in the drug effect over time.

We base analyses on subjects, excluding 15 who were missing baseline data or verified EOT outcomes. Table 1 shows descriptive characteristics by treatment arm. Baseline factors were balanced across arms, except that the fraction of whites was significantly larger in the bupropion arm. Both self-reported and verified EOT quit rates were higher on bupropion. We first analyzed the quit status at EOT using logistic regression, with and without baseline covariates. We then analyzed daily quit status using longitudinal logistic models: ME with a random subject effect and GEE with exchangeable and independent correlations.

For comparability with standard analyses, we excluded time-varying covariates from the longitudinal models. Moreover, as the ORs of ME models have a different interpretation from the ORs of GEE models [ 17 ], we applied a scale factor to render the results comparable [ 18 , 19 ]. Results appear in Table 2.

Treatment effect ORs are similar for models with and without baseline covariates, and for the two standard logistic models using self-reported and verified EOT quit. The three longitudinal models yield similar ORs, as expected. Figure 1 suggests that the drug effect is larger in the middle of the treatment phase than at the end; because the longitudinal OR represents an average of drug effects across time, it is unsurprising that it is higher than the EOT OR from the standard analysis.

This is also illustrated in Figure 2 , where the two curves diverge somewhat in the middle weeks of treatment, implying that there was less relapse in the drug group during that period. Daily OR of the drug effect. Each dot represents the estimated OR from a simple logistic regression of the smoking status data from day j with only drug in the model.

Daily quit rate by treatment arm. The solid dashed line is the fraction of subjects who are smoke-free in the placebo bupropion arm. The CIs from the longitudinal models are not dramatically narrower than those from the standard analysis. Increasing intra-subject correlation improves the precision of estimates of within-subject contrasts such as time trends and changes from baseline but only harms the efficiency of between-subject contrasts such as randomization group ORs.

We also conducted analyses including the time-varying predictors daily and weekly smoking history, a time effect, and a drug-by-time interaction. We first compared the results under ME and GEE, for simplicity treating week as continuous and excluding the drug-by-week interaction. Moreover, estimates differ substantially between the two GEE models. GEE typically yields consistent estimates regardless of the working correlation structure, as long as the mean model is correctly specified and the missing observations are missing completely at random [ 23 ].

With few missing observations, the discrepancy between the two GEE models must indicate a problem in the mean model itself. Thus we henceforth use the ME model to evaluate history and time effects on daily quit probability. In an elaborated ME model, we included a categorical week variable taking the final week [week8] as the reference and a drug-by-week interaction. Results moreover indicate that quit probability gradually decreased over time, with the quit probability significantly higher in the first two weeks compared to the last week, and eventually leveling off in the final three weeks.

The drug effect is statistically significant in all weeks, varying somewhat in the first several weeks and becoming stable in the last two. We have presented longitudinal models for binary daily abstinence data in smoking cessation trials. Our approach uses the finest-level data on quit history commonly available and provides a nuanced description of the evolution of the outcomes. By comparison, the standard approach of summarizing the point prevalence abstinence at a single designated time ignores the bulk of the available information, calling into question the common practice of collecting daily smoking data [ 24 ].

The standard analysis of point-prevalence abstinence is subject to the arbitrary choice of assessment time. Although most studies conduct the assessment at EOT, the length of the recommended treatment phase varies by drug [ 25 ], and therefore the reported point prevalence abstinence could refer to 8, 10 or 12 weeks of treatment. Although meta-analysis suggests that the treatment effect is insensitive to study duration [ 26 ], because quit rates generally decline over time this practice can diminish cross-trial comparability. In contrast, longitudinal analysis models the daily quit probability, rendering the interpretation independent of other elements of the study design.

As the longitudinal analysis uses all time points in the treatment period, the drug effect OR represents an effect averaged across time, whereas the standard analysis reflects the effect only at EOT. Our data analysis revealed higher ORs with the longitudinal models, suggesting some variation of drug effect over time. Regardless of the estimated ORs, the corresponding CIs from longitudinal models are generally narrower than those from simple logistic models, with the size of the difference depending on the within-subject correlation [ 20 ].

A longitudinal analysis of daily smoking status has greater potential efficiency gain when some observations are missing, because it can include the available data from all randomized subjects, even those lost to follow-up, whereas the standard approach has to either exclude the drop-outs or assume that they continued to smoke. Although such an assumption is held to be conservative, there is an increasing awareness that its indiscriminate use may lead to bias and lack of comparability between studies [ 26 , 27 26].

Fortunately our example had few missing observations. An important advantage of longitudinal modeling is its ability to incorporate time-varying predictors. In some studies, treatment such as drug dose changes over time by design [ 28 ]. Even if the treatment is constant, including the treatment-by-time interaction allows us to test whether its effects change over time, as in our example. We investigated the influence of smoking history on later success by coding summary measures of history as time-varying predictors.

Our results suggest that history is an important independent predictor of future outcome. Generally, there is concern that including outcome history may over-adjust and thereby attenuate treatment main effects. In our results, the drug effect OR in the ME model was 2.

Possibly the history variable absorbed some of the treatment effect, resulting in the decreased OR. Still, the large size and strong statistical significance of the adjusted effect suggests that bupropion continues to have an effect no matter how long one has been taking it and regardless of its effect to date. Although the ME and GEE models work similarly in many cases, one must bear in mind that their regression coefficients have different interpretations [ 17 ]. The drug effect OR in the ME model represents the odds of the outcome for a person taking the drug compared to the same person not taking the drug.

Estimates from GEE are generally smaller than those from ME, and the attenuation increases with the between-subjects variability. After applying the scale factor as we illustrated, the two estimates are comparable [ 18 , 19 ]. A marginal approach like GEE is problematic when one wishes to model time-varying effects [ 21 , 22 ]. As shown in our example, GEE is unreliable when one includes the outcome histories as predictors. Moreover, ME is preferable when there is substantial dropout, because one can estimate it consistently with weaker assumptions on the missing-data mechanism [ 29 ].

A potential limitation of our analysis is that we used treatment phase data only. Given the logarithmic nature of relapse curves [ 30 ], outcomes at later follow-ups such as 6 or 12 months are considered superior indicators of treatment success. Commonly, however, daily smoking data are either not collected or are unreliable after EOT, limiting the use of daily data beyond that point.

Nevertheless, the analysis we presented provides a way to evaluate the dynamic process in the treatment period using all the available information; one may conduct separate analyses on later outcomes using standard methods. Another limitation of our analysis is its use of TLFB data, which are self-reported and thus may be inaccurate in a fraction of subjects who falsely report abstinence [ 31 ].

Nevertheless, our data suggest that the drug effect is similar whether we use self-reported or verified EOT point prevalence abstinence as the outcome Table 2. One possible reason is that subjects in both arms are equally likely to report false abstinence and therefore any biases are offsetting.

Models for daily abstinence would not be biased by subjects who under-report cigarette counts, as long as they do not claim abstinence on smoking days. TLFB data are typically collected every one or two weeks and are therefore potentially subject to recall bias, which may affect estimates of within-subject correlation if the subjects incorrectly report the same or very similar counts for all the days being recorded at each visit.

We observed the same large correlation, suggesting that it is real and not simply an artifact of recall bias. Moreover, the drug effect OR was similar to that from the daily data, with a slightly wider CI. This is expected because the efficiency gain from using daily vs. The advent of electronic diaries, allowing collection of cigarette counts by ecological momentary assessment, may obviate the need for daily summarization of cigarette counts. One could in principle obtain a more efficient analysis from the series of daily cigarette counts. Because such data are subject to severe heaping, in the form of over-reporting of round numbers of cigarettes smoked [ 5 ], there is substantial potential for bias in such analyses, and it has been considered preferable to use the daily abstinence indicators.

As we have suggested, another approach is to model the duration of abstinent and smoking episodes, incorporating the possibility of permanent recovery and relapse [ 6 ]. Such models, when estimated exclusively from treatment-period data, can make excellent predictions for remote long-term outcomes [ 32 ].

With current types of data, the day is still the smallest time interval, but as electronic recording devices become more common, it will be possible to measure inter-cigarette intervals to the second, providing for an even finer analysis. We have presented a strategy to model longitudinal daily smoking status data from smoking cessation trials. Compared to the standard analysis of EOT abstinence status, our approach permits more detailed modeling and more precise estimation of effects. In our example study a strong drug effect persisted in the treatment period, with its magnitude varying across time.

The National Cancer Institute supported the research of Drs. We are grateful to Dr. Lerman for permission to use the data, to the referees for constructive suggestions, and to Dr. Thomas Ten Have for enlightening discussions. Each subject can have up to 56 rows of data. All authors declare that they have no conflict of interest.

Clinical trial registration details: National Center for Biotechnology Information , U. Author manuscript; available in PMC Nov 1. Yimei Li , 1 E. Paul Wileyto , 2 and Daniel F. Author information Copyright and License information Disclaimer. The publisher's final edited version of this article is available at Addiction. See other articles in PMC that cite the published article.

Tolerance of Non-Smokers to Smokers: Analysis of statistics Tolerance of Non-Smokers to Smokers: Analysis of statistics
Tolerance of Non-Smokers to Smokers: Analysis of statistics Tolerance of Non-Smokers to Smokers: Analysis of statistics
Tolerance of Non-Smokers to Smokers: Analysis of statistics Tolerance of Non-Smokers to Smokers: Analysis of statistics
Tolerance of Non-Smokers to Smokers: Analysis of statistics Tolerance of Non-Smokers to Smokers: Analysis of statistics
Tolerance of Non-Smokers to Smokers: Analysis of statistics Tolerance of Non-Smokers to Smokers: Analysis of statistics
Tolerance of Non-Smokers to Smokers: Analysis of statistics Tolerance of Non-Smokers to Smokers: Analysis of statistics
Tolerance of Non-Smokers to Smokers: Analysis of statistics Tolerance of Non-Smokers to Smokers: Analysis of statistics

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